## 設定
verbose = False
check   = False

### 言語の割合の均等化
balanced = True

### LDA 用

## トピック数
n_topics = 10 # 30は多過ぎる？

## doc, term の設定
doc_type      = 'form'  # 変更不可
doc_attr      = 'spell' # spell か sound の選択
max_doc_size  = 12
##
term_size = 'character'
term_type = 'skippy2gram'
## skippy n-gram の結合範囲
max_distance_val = round(max_doc_size * 0.8)
print(f"max_distance_val: {max_distance_val}")

## ngram を包括的にするかどうか
ngram_is_inclusive = True

### DTM 構築

## term の最低頻度
term_min_freq = 2

## 高頻度 term の濫用指標: 大きくし過ぎないように．0.05 は十分に大きい
term_abuse_threshold = 0.04

max_distance_val: 10

import sys, os, random, re, glob
import pandas as pd
import pprint as pp
from functools import reduce

## load data to process
from pathlib import Path
import pprint as pp

wd = Path(".")
dirs = [ x for x in wd.iterdir() if x.is_dir() and not x.match(r"plot*") ]
if verbose:
    print(f"The following {len(dirs)} directories are potential targets:")
    pp.pprint(dirs)

## list up files in target directory 
wd = Path(".")
target_dir = "data-words" # can be changed
target_files = sorted(list(wd.glob(f"{target_dir}/*.csv")))
#
print(f"\n{target_dir} contains {len(target_files)} files to process")
pp.pprint(target_files)

data-words contains 20 files to process
[PosixPath('data-words/base-sound-English-r6e-originals.csv'),
 PosixPath('data-words/base-sound-French-r0-opendic-s900.csv'),
 PosixPath('data-words/base-sound-German-r1a-original.csv'),
 PosixPath('data-words/base-spell-Arabic-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-Chinese-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-English-r6e-originals.csv'),
 PosixPath('data-words/base-spell-Esperanto-r0-orginal.csv'),
 PosixPath('data-words/base-spell-Finnish-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-French-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-German-r1a-originals.csv'),
 PosixPath('data-words/base-spell-Greek-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-Hungarian-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-Icelandic-r0-original.csv'),
 PosixPath('data-words/base-spell-Irish-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-Italian-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-Japanese-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-Russian-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-Spanish-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-Swahili-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-Turkish-r0-1k-mc.csv')]

import pandas as pd

## データ型の辞書
types = re.split(r",\s+", "spell, sound, freq")
type_settings = { t : 0 for t in types }
print(type_settings)

## 言語名の辞書
lang_name_text = "arabic, bengali, chinese, english, esperanto, finnish, french, \
greek, galician, german, hungarian, icelandic, irish, italian, japanese, russian, spanish, swahili, turkish"

langs = re.split(r",\s*", lang_name_text)
#langs = "english esperanto french german russian swahili".split(" ")
#langs = "english esperanto french german icelandic swahili".split(" ")
lang_settings = { lang : 0 for lang in langs }
print(f"{len(lang_settings.keys())} langs are available")
print(lang_settings)

## 辞書と統合
settings = { 'form': None, **type_settings, **lang_settings }
print(settings)

{'spell': 0, 'sound': 0, 'freq': 0}
19 langs are available
{'arabic': 0, 'bengali': 0, 'chinese': 0, 'english': 0, 'esperanto': 0, 'finnish': 0, 'french': 0, 'greek': 0, 'galician': 0, 'german': 0, 'hungarian': 0, 'icelandic': 0, 'irish': 0, 'italian': 0, 'japanese': 0, 'russian': 0, 'spanish': 0, 'swahili': 0, 'turkish': 0}
{'form': None, 'spell': 0, 'sound': 0, 'freq': 0, 'arabic': 0, 'bengali': 0, 'chinese': 0, 'english': 0, 'esperanto': 0, 'finnish': 0, 'french': 0, 'greek': 0, 'galician': 0, 'german': 0, 'hungarian': 0, 'icelandic': 0, 'irish': 0, 'italian': 0, 'japanese': 0, 'russian': 0, 'spanish': 0, 'swahili': 0, 'turkish': 0}

check = False

vars = list(settings.keys())
print(f"targe var names: {vars}")
d_parts = [ ]
for lang in langs:
    local_settings = settings.copy()
    if check:
        print(f"processing: {lang}")
    try:
        for f in [ f for f in target_files if lang.capitalize() in str(f) ]:
            print(f"reading: {f}")
            # 言語名の指定
            local_settings[lang] = 1
            # 型名の指定
            for type in vars:
                if type in str(f):
                    local_settings[type] = 1
            #
            try:
                d = pd.read_csv(f, encoding='utf-8', sep = ",", on_bad_lines = 'skip') # Crucially, ...= skip
                df = pd.DataFrame(d, columns = vars)
                for var in [ var for var in (types + langs) if var != 'freq' ]:
                    df[var] = local_settings[var]
                d_parts.append(df)
            except FileNotFoundError:
                pass
    except IndexError:
        pass
#
if verbose:
    d_parts

targe var names: ['form', 'spell', 'sound', 'freq', 'arabic', 'bengali', 'chinese', 'english', 'esperanto', 'finnish', 'french', 'greek', 'galician', 'german', 'hungarian', 'icelandic', 'irish', 'italian', 'japanese', 'russian', 'spanish', 'swahili', 'turkish']
reading: data-words/base-spell-Arabic-r0-1k-mc.csv
reading: data-words/base-spell-Chinese-r0-1k-mc.csv
reading: data-words/base-sound-English-r6e-originals.csv
reading: data-words/base-spell-English-r6e-originals.csv
reading: data-words/base-spell-Esperanto-r0-orginal.csv
reading: data-words/base-spell-Finnish-r0-1k-mc.csv
reading: data-words/base-sound-French-r0-opendic-s900.csv
reading: data-words/base-spell-French-r0-1k-mc.csv
reading: data-words/base-spell-Greek-r0-1k-mc.csv
reading: data-words/base-sound-German-r1a-original.csv
reading: data-words/base-spell-German-r1a-originals.csv
reading: data-words/base-spell-Hungarian-r0-1k-mc.csv
reading: data-words/base-spell-Icelandic-r0-original.csv
reading: data-words/base-spell-Irish-r0-1k-mc.csv
reading: data-words/base-spell-Italian-r0-1k-mc.csv
reading: data-words/base-spell-Japanese-r0-1k-mc.csv
reading: data-words/base-spell-Russian-r0-1k-mc.csv
reading: data-words/base-spell-Spanish-r0-1k-mc.csv
reading: data-words/base-spell-Swahili-r0-1k-mc.csv
reading: data-words/base-spell-Turkish-r0-1k-mc.csv

## データ統合

raw_df = pd.concat(d_parts)
raw_df

## 文字数の列を追加

raw_df['size'] = [ len(x) for x in raw_df[doc_type] ]
raw_df

## 言語名= language の列を追加

check = False
language_vals = [ ]
for i, row in raw_df.iterrows():
    if check:
        print(row)
    for j, lang in enumerate(langs):
        if check:
            print(f"{i}: {lang}")
        if row[lang] == 1:
            language_vals.append(lang)
if verbose:
    print(language_vals)
    len(language_vals)
#
raw_df['language'] = language_vals
raw_df

## 言語の選別

select_languages = True

#selected_langs = re.split(r",\s*", "english, french, german, russian, swahili")
selected_langs = re.split(r",\s*",
                          "arabic, bengali, chinese, english, french, german, \
                          greek, hungarian, russian, japanese, turkish")

print(f"selected languages: {selected_langs}")

if select_languages:
    df_new = [ ]
    for lang in selected_langs:
        df_new.append(raw_df[raw_df[lang] == 1])
    raw_df = pd.concat(df_new)
#
raw_df

selected languages: ['arabic', 'bengali', 'chinese', 'english', 'french', 'german', 'greek', 'hungarian', 'russian', 'japanese', 'turkish']

## 文字数の分布
import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure()
ax = fig.add_subplot(1,1,1)

ax.hist(raw_df['size'], bins = 40)
ax.set_xlabel('length of doc')
ax.set_ylabel('freq')
plt.title(f"Length distribution for docs")
fig.show()

/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_73347/1088473461.py:12: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown
  fig.show()

## 長さで濾過

print(f"max doc size: {max_doc_size}")
original_size = len(raw_df)
raw_df = raw_df[raw_df['size'] < max_doc_size]
filtered_size = len(raw_df)
print(f"{original_size - filtered_size} cases removed")

max doc size: 12
414 cases removed

## 結果の検査 1

for lang in langs:
    print(raw_df[lang].value_counts())

arabic
0    17554
1      995
Name: count, dtype: int64
bengali
0    18549
Name: count, dtype: int64
chinese
0    17549
1     1000
Name: count, dtype: int64
english
0    10300
1     8249
Name: count, dtype: int64
esperanto
0    18549
Name: count, dtype: int64
finnish
0    18549
Name: count, dtype: int64
french
0    16733
1     1816
Name: count, dtype: int64
greek
0    17575
1      974
Name: count, dtype: int64
galician
0    18549
Name: count, dtype: int64
german
0    16984
1     1565
Name: count, dtype: int64
hungarian
0    17558
1      991
Name: count, dtype: int64
icelandic
0    18549
Name: count, dtype: int64
irish
0    18549
Name: count, dtype: int64
italian
0    18549
Name: count, dtype: int64
japanese
0    17549
1     1000
Name: count, dtype: int64
russian
0    17576
1      973
Name: count, dtype: int64
spanish
0    18549
Name: count, dtype: int64
swahili
0    18549
Name: count, dtype: int64
turkish
0    17563
1      986
Name: count, dtype: int64

## 結果の検査 2

for type in types:
    print(raw_df[type].value_counts())

spell
1    12829
0     5720
Name: count, dtype: int64
sound
1    11630
0     6919
Name: count, dtype: int64
freq
1.0                17569
1                    966
1 не                   1
1 то время как         1
1 северу               1
1 него                 1
1 будет                1
1 образом              1
1 мышь                 1
Name: count, dtype: int64

## 統合: 割合補正を適用

eng_reduct_factor = 0.2
if balanced:
    eng_df = raw_df[raw_df['english'] == 1]
    non_eng_df = raw_df[raw_df['english'] == 0]
    eng_reduced_df = eng_df.sample(round(len(eng_df) * eng_reduct_factor))
    raw_df = pd.concat([eng_reduced_df, non_eng_df])
raw_df

## データの指定

raw_df = raw_df[raw_df[doc_attr] == 1]
print(f"doc_attr: {doc_attr}")
raw_df

doc_attr: spell

## 結果の検査 3

for lang in langs:
    print(raw_df[lang].value_counts())

arabic
0    8562
1     995
Name: count, dtype: int64
bengali
0    9557
Name: count, dtype: int64
chinese
0    8557
1    1000
Name: count, dtype: int64
english
0    8681
1     876
Name: count, dtype: int64
esperanto
0    9557
Name: count, dtype: int64
finnish
0    9557
Name: count, dtype: int64
french
0    8572
1     985
Name: count, dtype: int64
greek
0    8583
1     974
Name: count, dtype: int64
galician
0    9557
Name: count, dtype: int64
german
0    8780
1     777
Name: count, dtype: int64
hungarian
0    8566
1     991
Name: count, dtype: int64
icelandic
0    9557
Name: count, dtype: int64
irish
0    9557
Name: count, dtype: int64
italian
0    9557
Name: count, dtype: int64
japanese
0    8557
1    1000
Name: count, dtype: int64
russian
0    8584
1     973
Name: count, dtype: int64
spanish
0    9557
Name: count, dtype: int64
swahili
0    9557
Name: count, dtype: int64
turkish
0    8571
1     986
Name: count, dtype: int64

## 順序のランダマイズし，基本データを決定

import sklearn.utils
df = sklearn.utils.shuffle(raw_df)

## ngram の追加

import sys
sys.path.append('..')

import re
import ngrams
import importlib
importlib.reload(ngrams)
import ngrams_skippy

bases = df[doc_type]

## 1gram 列の追加
#sep = r""
#unigrams = [ list(filter(lambda x: len(x) > 0, y)) for y in [ re.split(sep, z) for z in bases ] ]
unigrams = ngrams.gen_unigrams(bases, sep = r"", check = False)
if verbose:
    random.sample(unigrams, 5)
#
df['1gram'] = unigrams
#df.loc[:,'1gram'] = unigrams
df

## 2gram列の追加

bigrams = ngrams.gen_bigrams(bases, sep = r"", check = False)

## 包括的 2gram の作成
if ngram_is_inclusive:
    bigrams = [ [*b, *u] for b, u in zip(bigrams, unigrams) ]

if verbose:
    print(random.sample(bigrams, 3))

df['2gram'] = bigrams
if verbose:
    df

## 3gram列の追加

trigrams = ngrams.gen_trigrams(bases, sep = r"", check = False)

## 包括的 3gram の作成
if ngram_is_inclusive:
    trigrams = [ [ *t, *b ] for t, b in  zip(trigrams, bigrams) ]

if verbose:
    print(random.sample(trigrams, 3))

df['3gram'] = trigrams
if verbose:
    df

## skippy 2grams の生成

import sys
sys.path.append("..") # library path に一つ上の階層を追加

import ngrams_skippy

skippy_2grams = [ ngrams_skippy.generate_skippy_bigrams(x,
                                                         missing_mark = '…',
                                                         max_distance = max_distance_val, check = False)
                  for x in df['1gram'] ]

## 包括的 skippy 2-grams の生成
if ngram_is_inclusive:
    for i, b2 in enumerate(skippy_2grams):
        b2.extend(unigrams[i])
#
if verbose:
    random.sample(skippy_2grams, 3)

## skippy 2gram 列の追加

df['skippy2gram'] = skippy_2grams
df

## skippy 3grams の生成

#import sys
#sys.path.append("..") # library path に一つ上の階層を追加

import ngrams_skippy

skippy_3grams = [ ngrams_skippy.generate_skippy_trigrams(x,
                                                         missing_mark = '…',
                                                         max_distance = max_distance_val, check = False)
                  for x in df['1gram'] ]

## 包括的 skippy 3-grams の生成
if ngram_is_inclusive:
    for i, t2 in enumerate(skippy_3grams):
        t2.extend(skippy_2grams[i])
#
if verbose:
    random.sample(skippy_3grams, 3)

## skippy 3gram 列の追加

df['skippy3gram'] = skippy_3grams
df

## LDA 構築の基になる document-term matrix (dtm) を構築

from gensim.corpora.dictionary import Dictionary

bots = df[term_type]
diction = Dictionary(bots)

## 結果の確認
print(diction)

Dictionary<10074 unique tokens: ['の', '個', '個の', 'e', 'en']...>

## diction の濾過

import copy
diction_copy = copy.deepcopy(diction)

## filter適用: 実は諸刃の刃で，token数が少ない時には適用しない方が良い
print(f"min freq filter: {term_min_freq}")
print(f"abuse filter: {term_abuse_threshold}")

apply_filter = True
if apply_filter:
    diction_copy.filter_extremes(no_below = term_min_freq, no_above = term_abuse_threshold)

## check
print(diction_copy)

min freq filter: 2
abuse filter: 0.04
Dictionary<6183 unique tokens: ['の', '個', 'e…g', 'e…i', 'e…n']...>

## Corpus (gensim の用語では corpus) の構築

corpus = [ diction.doc2bow(bot) for bot in bots ]

## check
check = True
if verbose:
    sample_n = 5
    print(random.sample(corpus, sample_n))
#
print(f"Number of documents: {len(corpus)}")

Number of documents: 9557

## LDA モデルの構築

from gensim.models import LdaModel
#from tqdm import tqdm

## LDAモデル
print(f"Building LDA model with n_topics: {n_topics}")
lda = LdaModel(corpus, id2word = diction, num_topics = n_topics, alpha = 0.01)
#
print(lda) # print(..)しないと中身が見れない

Building LDA model with n_topics: 10
LdaModel<num_terms=10074, num_topics=10, decay=0.5, chunksize=2000>

%%capture --no-display

## LDA のtopic ごとに，関連度の高い term を表示

import pandas as pd

n_terms = 40 # topic ごとに表示する term 数の指定
topic_dfs = [ ]
for topic in range(n_topics):
    terms = [ ]
    for i, prob in lda.get_topic_terms(topic, topn = n_terms):
        terms.append(diction.id2token[ int(i) ])
    #
    topic_dfs.append(pd.DataFrame([terms], index = [ f'topic {topic+1}' ]))
#
topic_term_df = pd.concat(topic_dfs)

## Table で表示
topic_term_df.T

%%capture --no-display

## pyLDAvis を使った結果 LDA の可視化: 階層クラスタリングより詳しい

import pyLDAvis

#installed_version = sys.version
installed_version = pyLDAvis.__version__

print(f"installed_version: {installed_version}")

if float(installed_version[:3]) > 3.1:
    import pyLDAvis.gensim_models as gensimvis
else:
    import pyLDAvis.gensim as gensimvis
#
pyLDAvis.enable_notebook()

#
lda_used     = lda
corpus_used  = corpus
diction_used = diction

## 実行パラメター
use_tSNE = False
if use_tSNE:
    vis = gensimvis.prepare(lda_used, corpus_used, diction_used, mds = 'tsne',
                            n_jobs = 1, sort_topics = False)
else:
    vis = gensimvis.prepare(lda_used, corpus_used, diction_used,
                            n_jobs = 1, sort_topics = False)
#
pyLDAvis.display(vis)
## topic を表わす円の重なりが多いならn_topics が多過ぎる可能性がある．
## ただし2Dで重なっていても，3Dなら重なっていない可能性もある

## LDA がD に対して生成した topics の弁別性を確認

## 得られたtopics を確認
topic_dist = lda.get_topics()
if verbose:
    topic_dist

## 検査 1: topic ごとに分布の和を取る

print(topic_dist.sum(axis = 1))

[1.0000001  1.0000001  0.99999994 1.         0.99999994 1.
 0.99999994 1.         1.0000001  1.        ]

## 検査 2: 総和を求める: n_topics にほぼ等しいなら正常

print(topic_dist.sum())

10.000001

## term エンコード値の分布を確認

import matplotlib.pyplot as plt

plt.figure(figsize = (4,5))

sampling_rate = 0.3
df_size = len(topic_dist)
sample_n = round(df_size * sampling_rate)
topic_sampled = random.sample(list(topic_dist), sample_n)

T = sorted([ sorted(x, reverse = True) for x in topic_sampled ])

plt.plot(T, range(len(T)))
plt.title("Distribution of sorted values ({sample_n} samples) for topic/term encoding")
plt.show()

## tSNE を使った topics のグループ化 (3D)

from sklearn.manifold import TSNE
import numpy as np

## tSNE のパラメターを設定
## n_components は射影先の空間の次元: n_components = 3 なら3次元空間に射影
## perplexity は結合の強さを表わす指数で，値に拠って結果が代わるので，色々な値を試すと良い

#perplexity_val = 10 # 大き過ぎると良くない
top_perplexity_reduct_rate = 0.3
perplexity_val = round(len(topic_dist) * top_perplexity_reduct_rate)
topic_tSNE_3d = TSNE(n_components = 3, random_state = 0, perplexity = perplexity_val, n_iter = 1000)

## データに適用
top_tSNE_3d_fitted = topic_tSNE_3d.fit_transform(np.array(topic_dist))

## Plotlyを使って tSNE の結果の可視化 (3D)

#import plotly.express as pex
import plotly.graph_objects as go
import numpy as np

top_tSNE = top_tSNE_3d_fitted
fig = go.Figure(data = [go.Scatter3d(x = top_tSNE[:,0], y = top_tSNE[:,1], z = top_tSNE[:,2],
                                     mode = 'markers')])

## 3D 散布図にラベルを追加する処理は未実装
title_val = f"3D tSNE view for LDA (#topics: {n_topics}, doc: {doc_type}, term: {term_type})"
fig.update_layout(autosize = False,
                  width = 600, height = 600, title = title_val)
fig.show()

## 構築した LDA モデルを使って文(書)を分類する

## .get_document_topics(..) は　minimu_probability = 0としないと
## topic の値が小さい場合に値を返さないので，

## パラメター

ntopics = n_topics # LDA の構築の最に指定した値を使う

check      = False
encoding = [ ]
for i, row in df.iterrows():
    if check:
        print(f"row: {row}")
    doc = row[doc_type]
    bot = row[term_type]
    ## get_document_topics(..) では　minimu_probability = 0 としないと
    ## 値が十分に大きな topics に関してだけ値が取れる
    enc = lda.get_document_topics(diction.doc2bow(bot), minimum_probability = 0)
    if check:
        print(f"enc: {enc}")
    encoding.append(enc)
#
len(encoding)

9557

## enc 列の追加

#df['enc'] = np.array(encoding) # This flattens arrays
#df['enc'] = list(encoding) # ineffective
df['enc'] = [ list(map(lambda x: x[1], y)) for y in encoding ]
if verbose:
    df['enc']

## エンコーディングのstd の分布を見る

from scipy.stats import tstd
from matplotlib import pyplot as plt

plt.figure(figsize = (6,4))

std_data = [ tstd(x) for x in df['enc'] ]
plt.hist(std_data)

plt.title("Distribution of standard deviations")
plt.show()

## doc のエンコーディング

## 一様分布の事例を除外

from scipy.stats import tstd # standard deviation の計算用

print(f"{len(df)} instances before filtering")

check = False

doc_enc = df['enc']
max_std = max([ tstd(x) for x in doc_enc])
if check: print(f"std max: {max_std}")

min_std = min([ tstd(x) for x in doc_enc])
if check: print(f"std min: {min_std}")

first_min_std = list(sorted(set([ tstd(x) for x in doc_enc])))[-0]
print(f"std 1st min: {first_min_std}")

second_min_std = list(sorted(set([ tstd(x) for x in doc_enc])))[-1]
print(f"std 2nd min: {second_min_std}")

9557 instances before filtering
std 1st min: 0.0
std 2nd min: 0.31571009800900685

## df_filtered の定義

## 閾値は2番目に小さい値より小さく最小値よりは大きな値であるべき
std_threshold = second_min_std / 4 # 穏健な値を得るために4で割った
print(f"std_threshold: {std_threshold}")

## Rっぽい次のコードは通らない
#df_filtered = df[ df['encoding'] > std_threshold ]

## 通るのは次のコード: Creating a list of True/False and apply it to DataFrame 
std_tested = [ False if tstd(x) < std_threshold else True for x in df['enc'] ]
df_filtered = df[ std_tested ]
#
print(f"{len(df_filtered)} instances after filtering ({len(df) - len(df_filtered)} instances removed)")

std_threshold: 0.07892752450225171
8974 instances after filtering (583 instances removed)

## doc エンコード値の分布を確認

sample_n = 50
E = sorted([ sorted(x, reverse = True) for x in df_filtered['enc'].sample(sample_n) ])

plt.figure(figsize = (5,5))
plt.plot(E, range(len(E)))
plt.title(f"Distribution of sorted encoding values for sampled {sample_n} docs")
plt.show()

len(df_filtered['language'])

8974

df_filtered['language'].value_counts

<bound method IndexOpsMixin.value_counts of 695     japanese
755      turkish
550        greek
401      chinese
198    hungarian
         ...    
942        greek
657      turkish
502     japanese
546       arabic
151      russian
Name: language, Length: 8974, dtype: object>

## tSNE 用の事例サンプリング = tSNE_df の定義

tSNE_sampling = True
tSNE_sampling_rate = 0.33
if tSNE_sampling:
    tSNE_df_original = df_filtered.copy()
    sample_n = round(len(tSNE_df_original) * tSNE_sampling_rate)
    tSNE_df = tSNE_df_original.sample(sample_n)
    print(f"tSNE_df has {len(tSNE_df)} rows after sampling")
else:
    tSNE_df = df_filtered

tSNE_df has 2961 rows after sampling

tSNE_df.columns

Index(['form', 'spell', 'sound', 'freq', 'arabic', 'bengali', 'chinese',
       'english', 'esperanto', 'finnish', 'french', 'greek', 'galician',
       'german', 'hungarian', 'icelandic', 'irish', 'italian', 'japanese',
       'russian', 'spanish', 'swahili', 'turkish', 'size', 'language', '1gram',
       '2gram', '3gram', 'skippy2gram', 'skippy3gram', 'enc'],
      dtype='object')

tSNE_df['language'].value_counts

<bound method IndexOpsMixin.value_counts of 385       french
175    hungarian
110        greek
179       arabic
711     japanese
         ...    
510    hungarian
648      russian
677      chinese
577       arabic
615        greek
Name: language, Length: 2961, dtype: object>

## tSNE の結果の可視化: Plotly を使った 3D 描画

import numpy as np
from sklearn.manifold import TSNE as tSNE

import plotly.express as pex
import plotly.graph_objects as go
import matplotlib.pyplot as plt

## tSNE のパラメターを設定

perplexity_max_val = round(len(tSNE_df)/4)

for perplexity_val in range(5, perplexity_max_val, 60):
    
    ## tSNE 事例の生成
    tSNE_3d_varied = tSNE(n_components = 3, random_state = 0, perplexity = perplexity_val, n_iter = 1000)

    ## データに適用
    doc_enc = np.array(list(tSNE_df['enc']))
    doc_tSNE_3d_varied = tSNE_3d_varied.fit_transform(doc_enc)

    T = zip(doc_tSNE_3d_varied[:,0], doc_tSNE_3d_varied[:,1], doc_tSNE_3d_varied[:,2],
            tSNE_df['language']) # zip(..)が必要
    df = pd.DataFrame(T, columns = ['D1', 'D2', 'D3', 'language'])

    ## 作図
    fig = go.Figure()
    for lang in np.unique(df['language']):
        part = df[df['language'] == lang]
        fig.add_trace(
            go.Scatter3d(
                x = part['D1'], y = part['D2'], z = part['D3'],
                name = lang, mode = 'markers', marker = dict(size = 6),
                showlegend = True
            )
        )

    title_val = f"tSNE 3D map (ppl: {perplexity_val}) of '{doc_attr}'s encoded\n by LDA ({n_topics} topics, {term_type})"
    fig.update_layout(title = dict(text = title_val),
                      autosize = False, width = 600, height = 600,)
    fig.show()

## 階層クラスタリングのための事例のサンプリング

hc_sampling_rate = 0.1 # 大きくし過ぎると図が見にくい
df_size = len(tSNE_df)
hc_sample_n = round(df_size * hc_sampling_rate)
hc_df = tSNE_df.sample(hc_sample_n)
##
print(f"{hc_sample_n} rows are sampled")
hc_df['language'].value_counts()

296 rows are sampled

language
hungarian    41
russian      39
arabic       35
french       32
greek        31
turkish      29
japanese     27
english      26
german       20
chinese      16
Name: count, dtype: int64

## 日本語表示のための設定
#import matplotlib.pyplot as plt
#plt.rcParams["font.family"] = "Hiragino Sans" # Windows は別のフォント名を指定する必要がある
#plt.rcParams["font.family"] = "Lucida Sans Unicode"

## doc 階層クラスタリングの実行

import numpy as np
import plotly
import matplotlib.pyplot as plt
## 次の設定は arabic が文字化けする
#plt.rcParams["font.family"] = "Hiragino Sans" # Windows は別のフォント名を指定する必要がある
#plt.rcParams["font.family"] = "Lucida Sans Unicode"

from scipy.cluster.hierarchy import dendrogram, linkage

## 距離行列の生成
Enc = list(hc_df['enc'])
linkage = linkage(Enc, method = 'ward', metric = 'euclidean')

## 描画サイズの指定
plt.figure(figsize = (5, round(len(hc_df) * 0.15))) # This needs to be run here, before dendrogram construction.

## 事例ラベルの生成
label_vals = [ x[:max_doc_size] for x in list(hc_df[doc_type]) ] # truncate doc keys

## 樹状分岐図の作成
dendrogram(linkage, orientation = 'left', labels = label_vals, leaf_font_size = 7)

## 描画
plt.title(f"Hierarchical clustering of (sampled) {len(hc_df)} (= {100 * hc_sampling_rate}%) {doc_attr}s as docs\n \
    encoded via LDA ({n_topics} topics) with {term_type} as terms")

## ラベルに language に対応する色を付ける
lang_colors = { lang_name : i for i, lang_name in enumerate(np.unique(hc_df['language'])) }
ax = plt.gca()
for ticker in ax.get_ymajorticklabels():
    form = ticker.get_text()
    row = hc_df.loc[hc_df[doc_type] == form]
    #lang = row['language']
    lang = row['language'].to_string().split()[-1] # trick
    try:
        lang_id = lang_colors[lang]
    except (TypeError, KeyError):
        print(f"color encoding error at: {lang}")
    #
    ticker.set_color(plotly.colors.qualitative.Plotly[lang_id]) # id の基数調整
#
plt.show()

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## tSNE の結果の可視化 (2D)

#import seaborn as sns
import numpy as np
import plotly
import plotly.express as pex
import matplotlib.pyplot as plt
from adjustText import adjust_text

## tSNE 事例の生成
perplexity_selected = 250
tSNE_3d = tSNE(n_components = 3, random_state = 0, perplexity = perplexity_selected, n_iter = 1000)

## データに適用
doc_enc = np.array(list(tSNE_df['enc']))
doc_tSNE_3d = tSNE_3d.fit_transform(doc_enc)

T = zip(doc_tSNE_3d[:,0], doc_tSNE_3d[:,1], doc_tSNE_3d[:,2],
        tSNE_df['language']) # zip(..)が必要
df = pd.DataFrame(T, columns = ['D1', 'D2', 'D3', 'language'])

## 描画
## 次の設定は arabic が文字化けする
#plt.rcParams["font.family"] = "Hiragino Sans" # Windows は別のフォント名を指定する必要がある
#plt.rcParams["font.family"] = "Lucida Sans Unicode"
plt.figure(figsize = (5, 5))
plt.set_colors = pex.colors.qualitative.Plotly
    
for r in [ np.roll([0,1,2], -i) for i in range(0,3) ]:
    if check:
        print(r)
    X, Y = df.iloc[:,r[0]], df.iloc[:,r[1]]
    gmax = max(X.max(), Y.max())
    gmin = min(X.min(), Y.min())
    plt.xlim(gmin, gmax)
    plt.ylim(gmin, gmax)

    colormap = pex.colors.qualitative.Plotly
    lang_list = list(np.unique(tSNE_df['language']))
    cmapped = [ colormap[lang_list.index(lang)] for lang in df['language'] ]
    scatter = plt.scatter(X, Y, s = 40, c = cmapped, edgecolors = 'w')

    ## 文字を表示する事例のサンプリング
    lab_sampling_rate = 0.02
    lab_sample_n = round(len(tSNE_df) * lab_sampling_rate)
    sampled_keys = [ doc[:max_doc_size] for doc in random.sample(list(tSNE_df[doc_type]), lab_sample_n) ]

    ## labels の生成
    texts = [ ]
    for x, y, s in zip(X, Y, sampled_keys):
        texts.append(plt.text(x, y, s, size = 9, color = 'blue'))

    ## label に repel を追加: adjustText package の導入が必要
    adjust_text(texts, force_points = 0.2, force_text = 0.2,
                expand_points = (1, 1), expand_text = (1, 1),
                arrowprops = dict(arrowstyle = "-", color = 'black', lw = 0.5))
    #

    plt.title(f"tSNE (ppl: {perplexity_selected}) 2D map of {len(tSNE_df)} {doc_attr}s via LDA ({term_type}; {n_topics} topics)")
    #plt.legend(np.unique(cmapped))
    plt.show()

/Volumes/K/opt/miniconda3/lib/python3.10/site-packages/adjustText/__init__.py:564: UserWarning:

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/Volumes/K/opt/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning:

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/Volumes/K/opt/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning:

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/Volumes/K/opt/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning:

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	topic 1	topic 2	topic 3	topic 4	topic 5	topic 6	topic 7	topic 8	topic 9	topic 10
0	о	i	α	ο	ا	ы	н	e	e	u
1	е	m	ρ	α	ل	а	ي…ة	a	r	s
2	а	mi	π	ε	م	е	а	n	s	k
3	т	ー	τ	ι	ر	з	a…f	t	u	ü
4	с	f	σ	τ	ة	az	и	r	i	a
5	л	い	η	ν	ي	е…е	т	l	o	l
6	к	ト	ω	κ	ال	н	o…p	i	t	ı
7	и	ス	γ	μ	ب	л…н	一	s	c	r
8	р	il	ή	ρ	ت	ый	к…т	o	l	z
9	н	ッ	φ	λ	ع	р	с…я	g	f	m
10	д	m…i	н…е	π	د	μο	不	k	p	d
11	в	ク	ρα	σ	ق	ны	غ	m	d	v
12	ь	の	ά	ί	و	п…е	ин	e…e	u…e	e
13	п	ル	π…ρ	ς	ن	за	pp	d	s…e	n
14	о…о	ン	е	ό	س	в…е	に	a…e	r…e	a…ı
15	у	g…g	по	ά	ح	п…а	ни	en	i…e	é
16	м	f…m	α…ί	η	ف	л…е	u	b	re	y
17	о…а	di	π…η	υ	ك	w…i	up	h	s…r	un
18	я	l	ие	δ	ا…ة	ре	d…g	c	er	h
19	о…е	た	е…е	ή	ج	е…л	ме	y	n	ü…ü
20	ол	i…i	ρ…η	ύ	ть	ел	н…е	e…n	o…e	u…a
21	о…н	し	ί	γ	ا…ا	α	子	a…a	e…e	sz
22	с…о	m…e	στ	έ	ط	т…р	ад	z	p…e	i
23	о…ь	m…d	ни	χ	أ	а…н	нт	er	c…e	b
24	г	π…ω	ση	θ	т	а…ы	м…н	i…e	u…r	k…ı
25	о…т	レ	ρ…σ	τ…ο	ل…ا	ер	по	a…t	r…s	u…u
26	б	イ	ρ…α	ε…ο	ь	н…а	ли	e…t	h	a…s
27	й	s	αρ	ος	п…т	п	на	n…e	e…s	au
28	о…и	fa	д…е	ει	ا…ر	й	اح	a…n	é	b…ü
29	с…а	a	α…τ	α…ό	с…ь	а…а	д…а	p	se	k…s
30	ст	m…l	ير	α…ι	р	а…л	н…т	r…e	p…i	ç
31	е…о	η…ο	α…α	α…τ	ل…ة	τι	н…с	v	s…i	t
32	то	e	α…η	ρ…ο	ا…ل	ά…μ	у	an	e…r	ö
33	ро	ア	α…ρ	る	ا…ي	μ	ут	r…n	o…r	ı…ı
34	ж	h…z	ε	α…ο	а…ь	п…н	он	in	ε	ő
35	ч	j…t	πα	π…ι	ل…ل	е…ы	н…р	t…e	o…s	s…z
36	ен	しい	τ…α	π…ο	р…т	و	о…я	e…i	es	к…л
37	с…е	f…i	γ…α	ο…ο	п…ь	л…ы	а…с	te	p…s	mu
38	ка	ем	μ	ου	и	μ…τ	وا	ar	ou	k…r
39	п…о	こ	н	κα	ل…ي	w…d	ц	t…n	t…e	k…k

	form	spell	sound	freq	arabic	bengali	chinese	english	esperanto	finnish	...	german	hungarian	icelandic	irish	italian	japanese	russian	spanish	swahili	turkish
0	كما	1	0	1.0	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
1	أنا	1	0	1.0	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2	له	1	0	1.0	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
3	أن	1	0	1.0	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
4	هو	1	0	1.0	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
994	burun	1	0	1	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
995	çoğul	1	0	1	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
996	öfke	1	0	1	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
997	iddia	1	0	1	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
998	kıta	1	0	1	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1

	form	spell	sound	freq	arabic	bengali	chinese	english	esperanto	finnish	...	icelandic	irish	italian	japanese	russian	spanish	swahili	turkish	size	language
0	كما	1	0	1.0	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	3	arabic
1	أنا	1	0	1.0	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	3	arabic
2	له	1	0	1.0	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	2	arabic
3	أن	1	0	1.0	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	2	arabic
4	هو	1	0	1.0	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	2	arabic
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
994	burun	1	0	1	0	0	0	0	0	0	...	0	0	0	0	0	0	0	1	5	turkish
995	çoğul	1	0	1	0	0	0	0	0	0	...	0	0	0	0	0	0	0	1	5	turkish
996	öfke	1	0	1	0	0	0	0	0	0	...	0	0	0	0	0	0	0	1	4	turkish
997	iddia	1	0	1	0	0	0	0	0	0	...	0	0	0	0	0	0	0	1	5	turkish
998	kıta	1	0	1	0	0	0	0	0	0	...	0	0	0	0	0	0	0	1	4	turkish

	form	spell	sound	freq	arabic	bengali	chinese	english	esperanto	finnish	...	icelandic	irish	italian	japanese	russian	spanish	swahili	turkish	size	language
3454	ɛkstɹə	0	1	1	0	0	0	1	0	0	...	0	0	0	0	0	0	0	0	6	english
3915	transcribes	1	1	1.0	0	0	0	1	0	0	...	0	0	0	0	0	0	0	0	11	english
1223	kəlɪɡɹəfɪ	0	1	1	0	0	0	1	0	0	...	0	0	0	0	0	0	0	0	9	english
1318	kɛmp	0	1	1	0	0	0	1	0	0	...	0	0	0	0	0	0	0	0	4	english
3732	tagalog	1	1	1.0	0	0	0	1	0	0	...	0	0	0	0	0	0	0	0	7	english
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
994	burun	1	0	1	0	0	0	0	0	0	...	0	0	0	0	0	0	0	1	5	turkish
995	çoğul	1	0	1	0	0	0	0	0	0	...	0	0	0	0	0	0	0	1	5	turkish
996	öfke	1	0	1	0	0	0	0	0	0	...	0	0	0	0	0	0	0	1	4	turkish
997	iddia	1	0	1	0	0	0	0	0	0	...	0	0	0	0	0	0	0	1	5	turkish
998	kıta	1	0	1	0	0	0	0	0	0	...	0	0	0	0	0	0	0	1	4	turkish

	form	spell	sound	freq	arabic	bengali	chinese	english	esperanto	finnish	...	irish	italian	japanese	russian	spanish	swahili	turkish	size	language	1gram
695	個の	1	0	1	0	0	0	0	0	0	...	0	0	1	0	0	0	0	2	japanese	[個, の]
755	zengin	1	0	1	0	0	0	0	0	0	...	0	0	0	0	0	0	1	6	turkish	[z, e, n, g, i, n]
550	κλάσμα	1	0	1	0	0	0	0	0	0	...	0	0	0	0	0	0	0	6	greek	[κ, λ, ά, σ, μ, α]
401	高	1	0	1	0	0	1	0	0	0	...	0	0	0	0	0	0	0	1	chinese	[高]
198	jelenleg	1	0	1	0	0	0	0	0	0	...	0	0	0	0	0	0	0	8	hungarian	[j, e, l, e, n, l, e, g]
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
657	oğlu	1	0	1	0	0	0	0	0	0	...	0	0	0	0	0	0	1	4	turkish	[o, ğ, l, u]
193	风	1	0	1	0	0	1	0	0	0	...	0	0	0	0	0	0	0	1	chinese	[风]
502	狩り	1	0	1	0	0	0	0	0	0	...	0	0	1	0	0	0	0	2	japanese	[狩, り]
546	موقف	1	0	1.0	1	0	0	0	0	0	...	0	0	0	0	0	0	0	4	arabic	[م, و, ق, ف]
151	там	1	0	1	0	0	0	0	0	0	...	0	0	0	1	0	0	0	3	russian	[т, а, м]

	form	spell	sound	freq	arabic	bengali	chinese	english	esperanto	finnish	...	russian	spanish	swahili	turkish	size	language	1gram	2gram	3gram	skippy2gram
695	個の	1	0	1	0	0	0	0	0	0	...	0	0	0	0	2	japanese	[個, の]	[個の, 個, の]	[個, の, 個の, 個, の]	[個の, 個, の]
755	zengin	1	0	1	0	0	0	0	0	0	...	0	0	0	1	6	turkish	[z, e, n, g, i, n]	[ze, en, ng, gi, in, z, e, n, g, i, n]	[zen, eng, ngi, gin, ze, en, ng, gi, in, z, e,...	[ze, z…n, z…g, z…i, en, e…g, e…i, e…n, ng, n…i...
550	κλάσμα	1	0	1	0	0	0	0	0	0	...	0	0	0	0	6	greek	[κ, λ, ά, σ, μ, α]	[κλ, λά, άσ, σμ, μα, κ, λ, ά, σ, μ, α]	[κλά, λάσ, άσμ, σμα, κλ, λά, άσ, σμ, μα, κ, λ,...	[κλ, κ…ά, κ…σ, κ…μ, κ…α, λά, λ…σ, λ…μ, λ…α, άσ...
401	高	1	0	1	0	0	1	0	0	0	...	0	0	0	0	1	chinese	[高]	[高, 高]	[高, 高, 高]	[高, 高]
198	jelenleg	1	0	1	0	0	0	0	0	0	...	0	0	0	0	8	hungarian	[j, e, l, e, n, l, e, g]	[je, el, le, en, nl, le, eg, j, e, l, e, n, l,...	[jel, ele, len, enl, nle, leg, je, el, le, en,...	[je, j…l, j…e, j…n, j…g, el, e…e, e…n, e…l, e…...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
657	oğlu	1	0	1	0	0	0	0	0	0	...	0	0	0	1	4	turkish	[o, ğ, l, u]	[oğ, ğl, lu, o, ğ, l, u]	[oğl, ğlu, oğ, ğl, lu, o, ğ, l, u]	[oğ, o…l, o…u, ğl, ğ…u, lu, o, ğ, l, u]
193	风	1	0	1	0	0	1	0	0	0	...	0	0	0	0	1	chinese	[风]	[风, 风]	[风, 风, 风]	[风, 风]
502	狩り	1	0	1	0	0	0	0	0	0	...	0	0	0	0	2	japanese	[狩, り]	[狩り, 狩, り]	[狩, り, 狩り, 狩, り]	[狩り, 狩, り]
546	موقف	1	0	1.0	1	0	0	0	0	0	...	0	0	0	0	4	arabic	[م, و, ق, ف]	[مو, وق, قف, م, و, ق, ف]	[موق, وقف, مو, وق, قف, م, و, ق, ف]	[مو, م…ق, م…ف, وق, و…ف, قف, م, و, ق, ف]
151	там	1	0	1	0	0	0	0	0	0	...	1	0	0	0	3	russian	[т, а, м]	[та, ам, т, а, м]	[там, та, ам, т, а, м]	[та, т…м, ам, т, а, м]

	form	spell	sound	freq	arabic	bengali	chinese	english	esperanto	finnish	...	spanish	swahili	turkish	size	language	1gram	2gram	3gram	skippy2gram	skippy3gram
695	個の	1	0	1	0	0	0	0	0	0	...	0	0	0	2	japanese	[個, の]	[個の, 個, の]	[個, の, 個の, 個, の]	[個の, 個, の]	[個の, 個の, 個, の]
755	zengin	1	0	1	0	0	0	0	0	0	...	0	0	1	6	turkish	[z, e, n, g, i, n]	[ze, en, ng, gi, in, z, e, n, g, i, n]	[zen, eng, ngi, gin, ze, en, ng, gi, in, z, e,...	[ze, z…n, z…g, z…i, en, e…g, e…i, e…n, ng, n…i...	[zen, ze…g, ze…i, ze…n, z…ng, z…n…i, z…n…n, z…...
550	κλάσμα	1	0	1	0	0	0	0	0	0	...	0	0	0	6	greek	[κ, λ, ά, σ, μ, α]	[κλ, λά, άσ, σμ, μα, κ, λ, ά, σ, μ, α]	[κλά, λάσ, άσμ, σμα, κλ, λά, άσ, σμ, μα, κ, λ,...	[κλ, κ…ά, κ…σ, κ…μ, κ…α, λά, λ…σ, λ…μ, λ…α, άσ...	[κλά, κλ…σ, κλ…μ, κλ…α, κ…άσ, κ…ά…μ, κ…ά…α, κ…...
401	高	1	0	1	0	0	1	0	0	0	...	0	0	0	1	chinese	[高]	[高, 高]	[高, 高, 高]	[高, 高]	[高, 高, 高]
198	jelenleg	1	0	1	0	0	0	0	0	0	...	0	0	0	8	hungarian	[j, e, l, e, n, l, e, g]	[je, el, le, en, nl, le, eg, j, e, l, e, n, l,...	[jel, ele, len, enl, nle, leg, je, el, le, en,...	[je, j…l, j…e, j…n, j…g, el, e…e, e…n, e…l, e…...	[jel, je…e, je…n, je…l, je…g, j…le, j…l…n, j…l...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
657	oğlu	1	0	1	0	0	0	0	0	0	...	0	0	1	4	turkish	[o, ğ, l, u]	[oğ, ğl, lu, o, ğ, l, u]	[oğl, ğlu, oğ, ğl, lu, o, ğ, l, u]	[oğ, o…l, o…u, ğl, ğ…u, lu, o, ğ, l, u]	[oğl, oğ…u, o…lu, ğlu, oğ, o…l, o…u, ğl, ğ…u, ...
193	风	1	0	1	0	0	1	0	0	0	...	0	0	0	1	chinese	[风]	[风, 风]	[风, 风, 风]	[风, 风]	[风, 风, 风]
502	狩り	1	0	1	0	0	0	0	0	0	...	0	0	0	2	japanese	[狩, り]	[狩り, 狩, り]	[狩, り, 狩り, 狩, り]	[狩り, 狩, り]	[狩り, 狩り, 狩, り]
546	موقف	1	0	1.0	1	0	0	0	0	0	...	0	0	0	4	arabic	[م, و, ق, ف]	[مو, وق, قف, م, و, ق, ف]	[موق, وقف, مو, وق, قف, م, و, ق, ف]	[مو, م…ق, م…ف, وق, و…ف, قف, م, و, ق, ف]	[موق, مو…ف, م…قف, وقف, مو, م…ق, م…ف, وق, و…ف, ...
151	там	1	0	1	0	0	0	0	0	0	...	0	0	0	3	russian	[т, а, м]	[та, ам, т, а, м]	[там, та, ам, т, а, м]	[та, т…м, ам, т, а, м]	[там, та, т…м, ам, т, а, м]

解析¶